Novel Bayesian Method to Derive Final Adjusted Values of Physicochemical Properties: Application to 74 Compounds

Accurate values of physicochemical
properties are essential for
screening semivolatile organic compounds for human and environmental
hazard and risk. In silico approaches for estimation
are widely used, but the accuracy of these and measured values can
be difficult to ascertain. Final adjusted values (FAVs) harmonize
literature-reported measurements to ensure consistency and minimize
uncertainty. We propose a workflow, including a novel Bayesian approach,
for estimating FAVs that combines measurements using direct and indirect
methods and in silico values. The workflow was applied
to 74 compounds across nine classes to generate recommended FAVs (FAVRs). Estimates generated by in silico methods
(OPERA, COSMOtherm, EPI Suite, SPARC, and polyparameter linear free
energy relationships (pp-LFER) models) differed by orders of magnitude
for some properties and compounds and performed systematically worse
for larger, more polar compounds. COSMOtherm and OPERA generally performed
well with low bias although no single in silico method
performed best across all compound classes and properties. Indirect
measurement methods produced highly accurate and precise estimates
compared with direct measurement methods. Our Bayesian method harmonized
measured and in silico estimated physicochemical
properties without introducing observable biases. We thus recommend
use of the FAVRs presented here and that the proposed Bayesian
workflow be used to generate FAVRs for SVOCs beyond those
in this study.